Choosing the right lattice-dsp algorithm
========================================

.. admonition:: Tutorial goal

   Map common DSP tasks to the package APIs, diagnostics, and validation scope.

.. note::

   New to the terminology? See the :doc:`lattice DSP concept map <../../algorithms/concept_map>` and the :doc:`causality/data-use guide <../../theory/causality_and_data_use>` for how online, offline, block, and MIMO examples should be read.

Context
-------

New users often know the problem they want to solve before they know the package
vocabulary.  This tutorial gives a compact decision table for stable IIR filtering,
adaptive identification, AR estimation, finite-Hankel reduction, Nehari/AAK-style
finite diagnostics, MIMO reduction, and matrix-lattice experiments.

Key idea and equations
----------------------

The organizing principle is to choose the coordinate system that matches the
constraint: reflection coefficients for scalar stability, AR recursions for
prediction, Hankel singular values for input-output memory, and state-space Markov
parameters for MIMO reduction.

How to read the result
----------------------

Use the printed table as a routing guide, then follow the linked examples for the selected algorithm family.

Run command
-----------

.. code-block:: bash

   python examples/algorithm_selection_demo.py

Source code
-----------

.. literalinclude:: ../../../examples/algorithm_selection_demo.py
   :language: python
   :linenos: